(Auteur) Land use change models are increasingly being used to evaluate the effect of land change on climate and biodiversity and to generate scenarios of deforestation. Although many methods are available to model land transition potentials, they are usually not user-friendly and require the specification of many parameters, making the task difficult for decision makers not familiar with the tools, as well as making the process difficult to interpret. In this article we propose a simple method for modeling transition potentials. SimWeight is an instance-based learning algorithm based on the logic of the K-Nearest Neighbor algorithm. The method identifies the relevance of each driver variable and predicts the transition potential of locations given known instances of change. A case study was used to demonstrate and validate the method. Comparison of results with the Multi-Layer Perceptron neural network (MLP) suggests that SimWeight performs similarly in its capacity to predict transition potentials, without the need for complex parameters. Another advantage of SimWeight is that it is amenable to parallelization for deployment on a cloud computing platform.

(Auteur) Floodway modeling has been performed extensively using HECRAS in floodplain studies. The model output is typically exported in GIS format and the floodway boundaries are overlaid on other spatial data to further edit or remodel the floodway to meet FEMA and local development requirements. In this article, a tightly coupled system comprised of a commercial GIS (ArcGIS) and HECRAS is presented. FloodwayGIS provides a comprehensive visual environment to edit, remodel, spatially analyze, and map floodway boundaries. The environment uses the HECRAS executable engine for every remodeling iteration. Four different encroachment editing options are provided within FloodwayGIS, which eliminates the need for a modeler to switch between HECRAS and GIS in the floodway modeling process, and results in savings of modeling time. FloodwayGIS also provides a mapping algorithm based on TIN intersection to produce smooth floodway boundaries that can be mapped in Digital Flood Insurance Rate Maps (DFIRMs) with minor editing.

(Auteur) In the context of geographical database generalization, this article deals with a generic process for road network selection. It is based on the geographical context, which is made explicit, and on the preservation of characteristic structure. It relies on literature that is adapted and collected. The first step is to detect significant structures and patterns of the road network such as roundabouts or highway interchanges. It allows the initial dataset to be enriched with explicit geographic structures that were implicit in the initial data. It helps both to make the geographical context explicit and to preserve characteristic structures. Then this enrichment is used as knowledge input for the following step: that is, the selection of roads in rural areas using graph theory techniques. After that, urban roads are selected by means of a block aggregation complex algorithm. Continuity between urban and rural areas is guaranteed by modelling continuity using strokes. Finally, the previously detected characteristic structures are typified to maintain their properties in the selected network. This automated process has been fully implemented on Clarity™ and tested on large datasets.